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Parent topic: AI Inspection
GS_MODEL_WAREHOUSE GS_MODEL_WAREHOUSE stores AI engine training models, including the models and detailed description of the training process.
GS_MODEL_WAREHOUSE GS_MODEL_WAREHOUSE stores AI engine training models, including the models and detailed description of the training process.
Progressive Knowledge | HUAWEI CLOUD
Ingesting ModelArts Logs to LTS LTS can collect logs from the AI development platform ModelArts. For details, see Deploying a Model as a Real-Time Service. Parent topic: Ingesting Cloud Service Logs to LTS
obs.ap-southeast-2.myhuaweicloud.com/solution-as-code-moudle/building-a-dify-llm-application-development-platform/document/DeepSeek%20-%20Internet%20Search%20-%20Knowledge%20Base.yml Figure 19 Importing a DSL file Figure 19 Workflow demo If the following message is displayed for a large AI
AI inference-accelerated ECSs use self-developed Ascend 310 processors for AI inference acceleration.
Managing Inspections Overview on Inspection Management Typical Scenario: Configuring Manual Inspection Tasks Managing Manual Inspections Typical Scenario: Configuring AI Inspection Tasks Managing AI Inspections (Self-Developed) Parent topic: Tenant Administrator Guide
The model can then be imported to create an AI application for centralized management. The application can be quickly deployed as a service.
ModelArts Best Practices This document provides ModelArts samples concerning a variety of scenarios and AI engines to help you quickly understand the process and operations of using ModelArts for AI development.
Obtaining an AI Application List Obtain AI applications by different search criteria. Obtaining Details About an AI Application Obtain AI application details by ID. Deleting an AI Application Delete AI applications by ID. All versions of the AI application can be deleted.
Supported AI Engines for ModelArts Inference If you import a model from OBS to ModelArts, the following AI engines and versions are supported.
Introduction Trained models under frameworks such as Caffe and TensorFlow can be converted into offline models supported by the Ascend AI processor by using the offline model generator (OMG).
Introduction Trained models under frameworks such as Caffe and TensorFlow can be converted into offline models supported by the Ascend AI processor by using the offline model generator (OMG).
Introduction Trained models under frameworks such as Caffe and TensorFlow can be converted into offline models supported by the Ascend AI processor by using the offline model generator (OMG).
Introduction Trained models under frameworks such as Caffe and TensorFlow can be converted into offline models supported by the Ascend AI processor by using the offline model generator (OMG).
It uses the Huawei-developed HiSilicon Hi3559A chip as the processor and works with the Atlas 200 AI Accelerator Card (optional) to provide 16 TOPS compute power on INT8 data. For details, see the Huawei Atlas 500 White Paper.
It uses the Huawei-developed HiSilicon Hi3559A chip as the processor and works with the Atlas 200 AI Accelerator Card (optional) to provide 16 TOPS compute power on INT8 data. For details, see the Huawei Atlas 500 White Paper.
After an AI application is deployed as a real-time service, you can use the API for inference.
CCE AI Suite (Ascend NPU) of v2.1.23 or later has been installed. For details about how to install the add-on, see CCE AI Suite (Ascend NPU). The Volcano Scheduler add-on has been installed. For details about the add-on version requirements, see Table 1.
